Clinical documentation improvement (CDI) is a method for healthcare providers to collect, track, and analyze patient data while providing valuable feedback to physicians. Even though electronic health record (EHR) systems have been around for many decades, they have only partially succeeded in dealing with complex data sets. There is a growing demand for improved CDI technology, and AI (Artificial Intelligence) tools are the solution. CDI programs backed by AI tools have the potential to transform healthcare organizations dramatically.
Significance of Accurate CDI
With the growing need for better data quality, accurate reimbursement, and improved patient outcomes, the significance of a robust clinical documentation improvement (CDI) has grown. Healthcare systems require accurate CDI to bridge the gaps in coding and maintain quality care.
CDIs must align to the level of care required for each patient, to the patient’s medical history and population health. Quality ratings, reimbursements, denials, and patient well-being will suffer due to inaccurate, missing, or non-specific documentation.
High-quality, precise, and error-free documentation will accurately reflect the complexity of the patient’s condition and the care provided. It improves diagnostic accuracy and specificity by ensuring critical patient data is captured, giving clarity to physicians, and assisting coders in generating quality data. It will facilitate an effective way of collecting data elements for better analysis and timely reporting of hospital data.
A robust CDI improves focus on accountability, quality of patient outcomes, and mortality rates.
How AI Solutions can Benefit CDI Programs
Handle Complex Workflows
AI and related technologies aid CDI teams and improve operational efficiency. For instance, consider a CDIS (Clinical Documentation Improvement Specialist) managing 100 patient charts. Out of these, typically, 30% would require clarifications or follow-up. Without technology, the CDIS would need to manually review all the charts to identify those that need enhancement. AI can help the CDIS automatically capture these complex cases and spend minimum time on them.- AI in Front-End CDI
By adding specificity where it is needed, AI can significantly improve the front end of the CDI process. For instance, AI powers a virtual medical assistant that augments the teams’ knowledge by using natural language processing (a subset of AI) for chart searches, simple EHR navigation, and intelligent data entry. Once the front-end documentation is complete and comprehensive, the back end becomes operational, giving physicians and coders more time to focus on other essential tasks. - AI in Back-End CDI
AI in the backend CDI ensures patients have 10% case coverage and that the documentation accurately reflects the care provided. The patient scope can be widened to include emergency operations, same-day surgeries, and physician office visitors through AI tools. AI reviews patient charts to spot clinical indicators, procedures, and diagnostic details and suggests opportunities through enhanced workflow. - Denial Prevention
CDIS and coders can rely on AI tools to capture specificity in CDIs and mitigate the risk of denials beforehand. AI-suggested appeal templates help CDI teams address denials effectively if or when they arise. - Impact Coding
AI significantly improves coding accuracy. It enables coder productivity by offering code suggestions, streamlining workflow, and cutting the coding time. It allows traceability of codes to make audits easy. - Real-time Data Access
The analytics and dashboard of the AI and NLP tools allow CDI professionals to access critical data in real-time. Physicians will be able to positively influence patient care through key performance indicators (KPIs), giving the organization a competitive advantage and helping them stay relevant. - Enhance Hierarchical Condition Categories (HCC)
HCCs identify complete in-patient and out-patient diagnoses and risks in various settings. HCC accuracy is crucial as it represents an entire patient population. AI tools support proactive management of HCCs and keep the organization compliant. It helps identify patients with HCC gaps and creates lists of who visited and who did not in a year. AI identifies query opportunities linked to the HCCs and alerts the clinical documentation improvement (CDI) staff to raise a query by attaching the relevant query note for easy reference. Query automation is done effectively without the CDI staff intervention. - Advanced Intelligence for Exceptional Results
AI and related technologies help healthcare organizations improve operational efficiency by bringing order and efficiency to CDI systems. It can help healthcare organizations meet the growing demands for authentic documentation, coding, and reimbursement. The AI technology’s automated and continuous review feature improves documentation consistency and achieves point-of-care accuracy.
AGS Health offers a technology-enabled, customized approach to revenue cycle management. Contact us today if you are ready to explore how advanced technologies can help your organization.
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